scholarly journals Debris-flow susceptibility assessment through cellular automata modeling: an example from 15–16 December 1999 disaster at Cervinara and San Martino Valle Caudina (Campania, southern Italy)

2003 ◽  
Vol 3 (5) ◽  
pp. 457-468 ◽  
Author(s):  
G. Iovine ◽  
S. Di Gregorio ◽  
V. Lupiano
Keyword(s):  
Cellular Automata ◽  
Debris Flow ◽  
Urban Areas ◽  
Debris Flows ◽  
Soil Cover ◽  
Southern Italy ◽  
Landslide Risk ◽  
Campania Region ◽  
Optimal Values ◽  
Debris Flow Susceptibility

Abstract. On 15–16 December 1999, heavy rainfall severely stroke Campania region (southern Italy), triggering numerous debris flows on the slopes of the San Martino Valle Caudina-Cervinara area. Soil slips originated within the weathered volcaniclastic mantle of soil cover overlying the carbonate skeleton of the massif. Debris slides turned into fast flowing mixtures of matrix and large blocks, downslope eroding the soil cover and increasing their original volume. At the base of the slopes, debris flows impacted on the urban areas, causing victims and severe destruction (Vittori et al., 2000). Starting from a recent study on landslide risk conditions in Campania, carried out by the Regional Authority (PAI –Hydrogeological setting plan, in press), an evaluation of the debris-flow susceptibility has been performed for selected areas of the above mentioned villages. According to that study, such zones would be in fact characterised by the highest risk levels within the administrative boundaries of the same villages ("HR-zones"). Our susceptibility analysis has been performed by applying SCIDDICA S3–hex – a hexagonal Cellular Automata model (von Neumann, 1966), specifically developed for simulating the spatial evolution of debris flows (Iovine et al., 2002). In order to apply the model to a given study area, detailed topographic data and a map of the erodable soil cover overlying the bedrock of the massif must be provided (as input matrices); moreover, extent and location of landslide source must also be given. Real landslides, selected among those triggered on winter 1999, have first been utilised for calibrating SCIDDICA S3–hex and for defining "optimal" values for parameters. Calibration has been carried out with a GIS tool, by quantitatively comparing simulations with actual cases: optimal values correspond to best simulations. Through geological evaluations, source locations of new phenomena have then been hypothesised within the HR-zones. Initial volume for these new cases has been estimated by considering the actual statistics of the 1999 landslides. Finally, by merging the results of simulations, a deterministic susceptibility zonation of the considered area has been obtained. In this paper, aiming at illustrating the potential for debris-flow hazard analyses of the model SCIDDICA S3–hex, a methodological example of susceptibility zonation of the Vallicelle HR-zone is presented.

scholarly journals Regional debris flow susceptibility analysis in mountainous peri-urban areas through morphometric and land cover indicators

2014 ◽  
Vol 14 (11) ◽  
pp. 3043-3064 ◽  
Author(s):  
M. C. Rogelis ◽  
M. Werner
Keyword(s):  
Land Cover ◽  
Debris Flow ◽  
Flood Risk ◽  
Urban Areas ◽  
Debris Flows ◽  
Flood Hazard ◽  
Flow Direction ◽  
Hazard Analysis ◽  
Principal Component ◽  
Debris Flow Susceptibility

Abstract. A method for assessing regional debris flow susceptibility at the watershed scale, based on an index composed of a morphometric indicator and a land cover indicator, is proposed and applied in 106 peri-urban mountainous watersheds in Bogotá, Colombia. The indicator of debris flow susceptibility is obtained from readily available information common to most peri-urban mountainous areas and can be used to prioritise watersheds that can subsequently be subjected to detailed hazard analysis. Susceptibility is considered to increase with flashiness and the possibility of debris flows occurring. Morphological variables recognised in the literature to significantly influence flashiness and occurrence of debris flows are used to construct the morphometric indicator by applying principal component analysis. Subsequently, this indicator is compared with the results of debris flow propagation to assess its capacity in identifying the morphological conditions of a watershed that make it able to transport debris flows. Propagation of debris flows was carried out using the Modified Single Flow Direction algorithm, following identification of source areas by applying thresholds identified in the slope–area curve of the watersheds. Results show that the morphometric variables can be grouped into four indicators: size, shape, hypsometry and (potential) energy, with energy being the component that best explains the capability of a watershed to transport debris flows. However, the morphometric indicator was found to not sufficiently explain the records of past floods in the study area. Combining the morphometric indicator with land cover indicators improved the agreement and provided a more reliable assessment of debris flow susceptibility in the study area. The analysis shows that, even if morphometric parameters identify a high disposition to the occurrence of debris flow, improving land cover can reduce the susceptibility. However, if favourable morphometric conditions are present but deterioration of the land cover in the watershed takes place, then the susceptibility to debris flow events increases. The indicator of debris flow susceptibility is useful in the identification of flood type, which is a crucial step in flood risk assessment especially in mountainous environments, and it can be used as input for prioritisation of flood risk management strategies at regional level and for the prioritisation and identification of detailed flood hazard analysis. The indicator is regional in scope, and therefore it is not intended to constitute a detailed assessment but to highlight watersheds that could potentially be more susceptible to damaging floods than others in the same region.

scholarly journals Rainstorm Magnitude and Debris Flows in Pyroclastic Deposits Covering Steep Slopes of Karst Reliefs in San Martino Valle Caudina (Campania, Southern Italy)

Water ◽  
2021 ◽  
Vol 13 (16) ◽  
pp. 2274
Author(s):  
Guido Leone ◽  
Pasquale Clemente ◽  
Libera Esposito ◽  
Francesco Fiorillo
Keyword(s):  
Time Series ◽  
Debris Flow ◽  
Debris Flows ◽  
Southern Italy ◽  
Distribution Functions ◽  
Annual Rainfall ◽  
Standard Normal Distribution ◽  
Relative Significance ◽  
Campania Region ◽  
Steep Slopes

Debris flows that have occurred in the area of San Martino Valle Caudina (Campania, Southern Italy) are described by geomorphological and hydrological analyses, focusing on the recent event of December 2019. This area can be considered a key example for studying debris-flow phenomena involving the pyroclastic mantle that covers the karstified bedrock along steep slopes. A hydrological analysis of the time series of the maximum annual rainfall, of durations of 1, 3, 6, 12 and 24 h, was carried out based on a new approach to assess rainstorm magnitude. It was quantified by measuring the deviation of the rainfall intensity from the normal conditions, within a specified time period. As the time series of annual maxima are typically skewed, a preliminary transformation is needed to normalize the distribution; to obtain the Z-value of the standard normal distribution, with mean µ = 0 and standard deviation σ = 1, different probability distribution functions were fitted to the actual data. A specific boxplot was used, with box width Z = ±1 and whiskers length Z = ±2. The deviations from these values provide the performance of the distribution fits. For the normalized time series, the rates shown by the trends and relative significance were investigated for the available time series of 11 rain gauges covering the Western–Central Campania region. The most critical condition for the debris-flow initiation appears to occur when a severe or extreme rainfall has a duration ≥ 12 h. The trend analysis did not detect statistically significant increases in the intensity of the rainfall of duration ≥ 6 h.

A probabilistic model for assessing debris flow propagation at regional scale: a case study in Campania region, Italy

2021 ◽  
Author(s):  
Luca Crescenzo ◽  
Gaetano Pecoraro ◽  
Michele Calvello ◽  
Richard Guthrie
Keyword(s):  
Debris Flow ◽  
Debris Flows ◽  
Regional Scale ◽  
Erosion And Deposition ◽  
Campania Region ◽  
Source Areas ◽  
Model Input ◽  
Travel Path ◽  
Debris Avalanches ◽  
Modelling Approach

<p>Debris flows and debris avalanches are rapid to extremely rapid landslides that tend to travel considerable distances from their source areas. Interaction between debris flows and elements at risk along their travel path may result in potentially significant destructive consequences. One of the critical challenges to overcome with respect to debris flow risk is, therefore, the credible prediction of their size, travel path, runout distance, and depths of erosion and deposition. To these purposes, at slope or catchment scale, sophisticated physically-based models, appropriately considering several factors and phenomena controlling the slope failure mechanisms, may be used. These models, however, are computationally costly and time consuming, and that significantly hinders their applicability at regional scale. Indeed, at regional scale, debris flows hazard assessment is usually carried out by means of qualitative approaches relying on field surveys, geomorphological knowledge, geometric features, and expert judgement.</p><p>In this study, a quantitative modelling approach based on cellular automata methods, wherein individual cells move across a digital elevation model (DEM) landscape following behavioral rules defined probabilistically, is proposed and tested. The adopted model, called LABS, is able to estimate erosion and deposition soil volumes along a debris flow path by deploying at the source areas autonomous subroutines, called agents, over a 5 m spatial resolution DEM, which provides the basic information to each agent in each time-step. Rules for scour and deposition are based on mass balance considerations and independent probability distributions defined as a function of slope DEM-derived values and a series of model input parameters. The probabilistic rules defined in the model are based on data gathered for debris flows and debris avalanches that mainly occurred in western Canada. This study mainly addresses the applicability and the reliability of this modelling approach to areas in southern Italy, in Campania region, historically affected by debris flows in pyroclastic soils. To this aim, information on inventoried debris flows is used in different study areas to evaluate the effect on the predictions of the model input parameter values, as well as of different native DEM resolutions.</p>

Mitigation of Debris Flows—Research and Practice in Hong Kong

2021 ◽  
Vol 27 (2) ◽  
pp. 231-243
Author(s):  
Ken K. S. Ho ◽  
Raymond C. H. Koo ◽  
Julian S. H. Kwan
Keyword(s):  
Hong Kong ◽  
Debris Flow ◽  
Debris Flows ◽  
Geotechnical Engineering ◽  
Mitigation Strategies ◽  
Mitigation Measures ◽  
Landslide Risk ◽  
Engineering Practice ◽  
Design Methodologies ◽  
Technical Framework

ABSTRACT Dense urban development on a hilly terrain coupled with intense seasonal rainfall and heterogeneous weathering profiles give rise to acute debris-flow problems in Hong Kong. The Geotechnical Engineering Office (GEO) of the Hong Kong SAR Government has launched a holistic research and development (R&D) programme and collaborated with various tertiary institutes and professional bodies to support the development of a comprehensive technical framework for managing landslide risk and designing debris-flow mitigation measures. The scope of the technical development work includes compilation of landslide inventories, field studies of debris flows, development and calibration of tools for landslide run-out modelling, back analysis of notable debris flows, physical and numerical modelling of the interaction between debris flows and mitigation measures, formulation of a technical framework for evaluating debris-flow hazards, and development of pragmatic mitigation strategies and design methodologies for debris-flow countermeasures. The work has advanced the technical understanding of debris-flow hazards and transformed the natural terrain landslide risk management practice in Hong Kong. New analytical tools and improved design methodologies are being applied in routine geotechnical engineering practice.

Debris-Flow Risk Assessment

2016 ◽  
Author(s):  
Matthias Jakob ◽  
Kris Holm ◽  
Scott McDougall
Keyword(s):  
Risk Assessment ◽  
Debris Flow ◽  
Debris Flows ◽  
Numerical Models ◽  
Risk Tolerance ◽  
Nuclear Industry ◽  
Decision Makers ◽  
Economic Losses ◽  
Landslide Risk ◽  
Frequency Magnitude

Debris flows are one of the most destructive landslide processes worldwide, given their ubiquity in mountainous areas occupied by human settlement or industrial facilities around the world. Given the episodic nature of debris flows, these hazards are often un- or under-recognized. Three fundamental components of debris-flow risk assessments include frequency-magnitude analysis, numerical scenario modeling, and consequence analysis to estimate the severity of damage and loss. Recent advances in frequency-magnitude analysis take advantage of developments in methods to estimate the age of deposits and size of past and potential future events. Notwithstanding, creating reliable frequency-magnitude relationships is often challenged by practical limitations to investigate and statistically analyze past debris-flow events that are often discontinuous, as well as temporally and spatially censored. To estimate flow runout and destructive potential, several models are used worldwide. Simple empirical models have been developed based on statistical geometric correlations, and two-dimensional and three-dimensional numerical models are commercially available. Quantitative risk assessment (QRA) methods for assessing public safety were developed for the nuclear industry in the 1970s and have been applied to landslide risk in Hong Kong starting in 1998. Debris-flow risk analyses estimate the likelihood of a variety of consequences. Quantitative approaches involve prediction of the annual probability of loss of life to individuals or groups and estimates of annualized economic losses. Recent progress in quantitative debris-flow risk analyses include improved methods to characterize elements at risk within a GIS environment and estimates of their vulnerability to impact. Improvements have also been made in how these risks are communicated to decision makers and stakeholders, including graphic display on conventional and interactive online maps. Substantial limitations remain, including the practical impossibility of estimating every direct and indirect risk associated with debris flows and a shortage of data to estimate vulnerabilities to debris-flow impact. Despite these limitations, quantitative debris-flow risk assessment is becoming a preferred framework for decision makers in some jurisdictions, to compare risks to defined risk tolerance thresholds, support decisions to reduce risk, and quantify the residual risk remaining following implementation of risk reduction measures.

scholarly journals Multiple Effects of Intense Meteorological Events in the Benevento Province, Southern Italy

Water ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1560 ◽  
Author(s):  
Paola Revellino ◽  
Luigi Guerriero ◽  
Neri Mascellaro ◽  
Francesco Fiorillo ◽  
Gerardo Grelle ◽  
...  
Keyword(s):  
Soil Erosion ◽  
Debris Flows ◽  
Southern Italy ◽  
Statistical Characteristics ◽  
Total Rainfall ◽  
Campania Region ◽  
Rainfall Events ◽  
Pattern Distribution ◽  
The Town ◽  
Flooded Areas

In October 2015, two intense rainfall events hit the central and southern regions of Italy and triggered a combination of different and widespread effects, including floods, landslides, and soil erosion. These outcomes devastated about 68 municipalities of the Benevento province (Campania region), killed two people, and caused millions of euros worth of damage to structures, infrastructures, and agriculture. The town of Benevento was one of the sectors most affected by overflow. Extensive areas characterized by flyschoid outcrops experienced widespread occurrences of soil erosion and landslides, and destructive, high-velocity debris flows (about 50) afflicted areas that had experienced heavy rainfall of higher intensity (total rainfall of 415.6 mm). In this study, the characteristics of these rainfall events and related geomorphological processes were determined by (i) analyzing the available rainfall data to identify the spatial pattern, distribution, and statistical characteristics of the two storms and (ii) mapping the storm effects, such as flooded areas, landslide types, and soil erosion. These effects were then related to the spatial distribution of the storms and the local geological and geomorphologic settings that drove their initiation and development.

scholarly journals Development of Nomogram for Debris Flow Forecasting Based on Critical Accumulated Rainfall in South Korea

Water ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 2181
Author(s):  
Nam ◽  
Lee ◽  
Kim
Keyword(s):  
South Korea ◽  
Debris Flow ◽  
Urban Areas ◽  
Debris Flows ◽  
Rainfall Intensity ◽  
Extreme Weather Events ◽  
Weather Events ◽  
Accumulated Rainfall ◽  
Landslides And Debris Flows ◽  
Heavy Rains

Climate change causes extreme weather events worldwide such as increasing temperatures and changing rainfall patterns. With South Korea facing growing damage from the increased frequency of localized heavy rains. In particular, its steep slope lands, including mountainous areas, are vulnerable to damage from landslides and debris flows. In addition, localized short-term heavy rains that occur in urban areas with extremely high intensity tend to lead a sharp increase in damage from soil-related disasters and cause huge losses of life and property. Currently, South Korea forecasts landslides and debris flows using the standards for forecasting landslides and heavy rains. However, as the forecasting is conducted separately for rainfall intensity and accumulated rainfall, this lacks a technique that reflects both amount and intensity of rainfall in an episode of localized heavy rainfall. In this study, aims to develop such a technique by collecting past cases of debris flow occurrences and rainfall events that accompanied debris flows to calculate the rainfall triggering index (RTI) reflecting accumulated rainfall and rainfall intensity. In addition, the RTI is converted into the critical accumulated rainfall (Rc) to use rainfall information and provide real-time forecasting. The study classifies the standards for flow debris forecasting into three levels: ALERT (10–50%), WARNING (50–70%), and EMERGENCY (70% or higher), to provide a nomogram for 6 h, 12 h, and 24 h. As a result of applying this classification into the actual cases of Seoul, Chuncheon, and Cheongju, it is found that about 2–4 h of response time is secured from the point of the Emergency level to the occurrence of debris flows.

scholarly journals Spatial Predictions of Debris Flow Susceptibility Mapping Using Convolutional Neural Networks in Jilin Province, China

Water ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2079
Author(s):  
Yang Chen ◽  
Shengwu Qin ◽  
Shuangshuang Qiao ◽  
Qiang Dou ◽  
Wenchao Che ◽  
...  
Keyword(s):  
Neural Networks ◽  
Debris Flow ◽  
Statistical Methods ◽  
Convolutional Neural Networks ◽  
Debris Flows ◽  
Susceptibility Mapping ◽  
Jilin Province ◽  
Support Vector ◽  
Validation Set ◽  
Debris Flow Susceptibility

Debris flows are a major geological disaster that can seriously threaten human life and physical infrastructures. The main contribution of this paper is the establishment of two–dimensional convolutional neural networks (2D–CNN) models by using SAME padding (S–CNN) and VALID padding (V–CNN) and comparing them with support vector machine (SVM) and artificial neural network (ANN) models, respectively, to predict the spatial probability of debris flows in Jilin Province, China. First, the dataset is randomly divided into a training set (70%) and a validation set (30%), and thirteen influencing factors are selected to build the models. Then, multicollinearity analysis and gain ratio methods are used to quantify the predictive ability of factors. Finally, the area under the receiver operatic characteristic curve (AUC) and statistical methods are utilized to measure the accuracy of the models. The results show that the S–CNN model gets the highest AUC value of 0.901 in the validation set, followed by the SVM model, the V–CNN model, and the ANN model. Three statistical methods also show that the S–CNN model produces minimum errors compared with other models. The S–CNN model is hailed as an important means to improve the accuracy of debris–flow susceptibility mapping and provides a reasonable scientific basis for critical decisions.

scholarly journals Creating a national scale debris flow susceptibility model for Great Britain: a GIS-based heuristic approach

2019 ◽  
Author(s):  
Emma J. Bee ◽  
Claire Dashwood ◽  
Catherine Pennington ◽  
Roxana L. Ciurean ◽  
Katy Lee
Keyword(s):  
Great Britain ◽  
Debris Flow ◽  
Debris Flows ◽  
Methodological Approach ◽  
Slope Angle ◽  
Transport Infrastructure ◽  
Stream Channels ◽  
National Scale ◽  
Susceptibility Model ◽  
Debris Flow Susceptibility

Abstract. Debris flows in Great Britain have caused damage to transport infrastructure, buildings, and disruption to businesses and communities. This study describes a GIS-based heuristic model developed by the British Geological Survey (BGS) to produce a national scale spatial assessment of debris flow susceptibility for Great Britain. The model provides information on the potential for debris flow occurrence using properties and characteristics of geological materials (permeability, material availability and characteristics when weathered), slope angle and proximity to stream channels as indicators of susceptibility. Building on existing knowledge, the model takes into account the presence or absence of glacial scouring. As determined by the team of geologists and geomorphologists, the model ranks the availability of debris material and slope as the two dominant factors important for potential debris flow initiation, however it also considers other factors such as geological controls on infiltration. The resultant model shows that over 90 % of the mapped debris flows in the BGS inventory occurred in areas with the highest potential for instability and approximately 6 % were attributed to areas where the model suggested that debris flows are unlikely or not thought to occur. Model validation in the Cairngorm Mountains indicated a better performance, with 93.50 % in the former and less than 3 % in the latter category. Although the quality of the input datasets and selected methodological approach bear limitations and introduce a number of uncertainties, overall, the proposed susceptibility model performs better than previous attempts, representing a useful tool in the hands of policy-makers, developers and engineers to support regional or national scale development action plans and disaster risk reduction strategies.

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